Language Log

Before I finish typing "red", Google is already suggesting "red herring", which is what I was looking for.

When I've barely begun to type "Philadelphia" or "Seattle" and only one "Walla", Google is already suggesting ""Philadelphia weather", "Seattle weather" and "Walla Walla weather", which is what I was looking for in each case.

If I want to check in on American Airlines, all I have to type is "ame", and — voilà! — there it is!

I was trying to think of a certain kind of Japanese tomb.  I typed in "Japanese tombs" and, remembering that these tombs resemble a keyhole, I added "k", and up popped "japanese keyhole tombs".

I do this kind of search hundreds of times every day, and I'm infinitely grateful to Google for enabling them and making them seem (on my end) so effortless.

Everybody seems to enjoy sharing dialect maps displaying the boundaries of different American regionalisms. So it was only a matter of time before this enticing form of data visualization got satirized. On Twitter, Josh Cagan takes it in an absurdist direction.

https://twitter.com/joshacagan/status/885384919047782400

There's a wonderful new podcast on linguistic matters that I highly recommend to all Language Log readers. It's called Lingthusiasm, and it's appropriately billed as "a podcast that's enthusiastic about linguistics." The podcast is co-hosted by Gretchen McCulloch and Lauren Gawne. You may know Gretchen from her All Things Linguistic blog or her posts on The (dearly departed) Toast about Internet language. Lauren is a postdoctoral fellow at SOAS and blogs at Superlinguo. There have been six episodes so far, and they're all worth a listen.

The following ghastly photographs of a rat that was caught stealing from a convenience store in Heyuan, Guangdong province have gone viral on Chinese social media.

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WARNING:  viewer discretion advised.

The photographs following the page break may be upsetting to some readers.

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Google has released a fun data visualization tool that shows changes in search interest over time for a variety of trending words, particularly new slang terms. In "The Year in Language 2016," you can see how frequently people searched for the definitions of words, in queries such as "selfie definition" or "define selfie." By this metric, the top 10 words for 2016 are: triggered, shook, juju, broccoli, woke, holosexual, shill, gaslighting, bigly, and SJW. You can also plot the search interest for more than 50 words from 2013 to 2016.

From an anonymous reader, who spotted this photograph on Instagram, where it was posted by nanorie, who has given her permission to repost it:

Starting around a year or two ago, the expression "Zhào jiārén 赵家人" ("Zhao family member") emerged as a coded reference for politically powerful and wealthy elites in contemporary Chinese society.  See Kiki Zhao's penetrating post on the NYT Sinosphere blog:

"Leveling Criticism at China’s Elite, Some Borrow Words From the Past" (1/4/16)

For the literary background of "Zhào jiārén 赵家人" ("Zhao family member"), see this post:

"Lu Xun and the Zhao family" (1/5/16)

After reading "A new English word" (11/30/16), Yixue Yang sent me the following interesting note on "lihai" ("awesome / awful") in action in China today:

On microblogs and on social media in China, it was well nigh universal to call the ruler of North Korea Jīn sān pàng 金三胖 ("Kim Third Fat" [referring to Kim Jung-un, third in the line of Kims following his father Kim Jung-il and his grandfather Kim Il-sung]) — until the North Korean government caught wind of it and complained to the Chinese government:

"North Korea begs China to stop calling Kim Jong Un fat" (FOX News, 11/15/16)

Today, Green's Dictionary of Slang (GDoS for short) launches its online version. This is excellent news, coming more than five years after Jonathon Green published the print edition of his exhaustive three-volume reference work. As I wrote in the New York Times Book Review at the time,

It's a never-ending challenge to keep up with the latest developments in the world of slang, but that is the lexicographer’s lot. Green plans to put his dictionary online for continuous revision, which is indeed the direction that many major reference works (including the O.E.D.) are now taking. In the meantime, his monument to the inventiveness of speakers from Auckland to Oakland takes its place as the pièce de résistance of English slang studies. To put it plain, it’s copacetic.

Despite some tough sledding along the way, GDoS now sees the light of day online. Below is Jonathon Green's announcement. (For more, read the coverage in Quartz, and also see the dictionary's blog.) The good news is that headwords, etymologies, and definitions are freely available through online searches, while the full entries, with voluminous citations for each sense of each word, are available for an annual subscription fee.

I received an exciting email this afternoon from Perry Alexis, the chief accountant for the Warren Buffett Foundation. It seems I have been picked to receive a $1,500,000 donation — not a grant for research or anything, but a donation. And I notice it came from an email address in Kazakhstan.

Christina Xu has written "A Field Guide to China's Most Indispensible Meme" (Motherboard, 8/1/16).  Her essay includes more than a dozen illustrations, the first of which is this one:

I was recently moved (screaming and struggling, as four strong men held me down by my arms and legs) to a new web-based university email system designed and run by Microsoft: Office 365. Naturally, it's ill-designed slow-loading crap, burdened by misfeatures and pointless pop-ups that I do not want popping up, and it fails to allow various elementary operations that I often need (every upgrade is a downgrade). But that is not my topic today. I want to note one special sad consequence of moving to an entirely new system: all my previous email system's Bayesian machine learning about spam classification has been lost. The Office 365 system has had hardly any data to learn from as yet, so I am seeing some of the stuff that would have been coming to me all along if it had not been caught by machine learning and dumped in the spam bin. And what has truly amazed me is the daily flow of advertising for spamferences and junk journals.